

import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case				:      Tuple   		=			get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
	from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__snake_case				:      Optional[int]   		=			25_60_47
__snake_case				:      Tuple   		=			25_61_45



@require_sentencepiece
@require_tokenizers
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Optional[Any]       					=			NllbTokenizer
						_A    :				Dict       					=			NllbTokenizerFast
						_A    :				Optional[Any]       					=			True
						_A    :				Any       					=			True
						_A    :				Optional[Any]       					=			{}





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									super().setUp()

									# We have a SentencePiece fixture for testing
									a_						:						Any 							= NllbTokenizer(_lowercase  ,      keep_accents=_lowercase    )
									tokenizer.save_pretrained(self.tmpdirname    )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Union[str, Any] 							= NllbTokenizer(_lowercase  ,      keep_accents=_lowercase    )

									a_						:						List[Any] 							= tokenizer.tokenize("""This is a test"""    )
									self.assertListEqual(_lowercase  ,      ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]    )

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(_lowercase    )  ,      [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,      )

									a_						:						List[str] 							= tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
									self.assertListEqual(
									    _lowercase  ,      [
									        SPIECE_UNDERLINE + """I""",
									        SPIECE_UNDERLINE + """was""",
									        SPIECE_UNDERLINE + """b""",
									        """or""",
									        """n""",
									        SPIECE_UNDERLINE + """in""",
									        SPIECE_UNDERLINE + """""",
									        """9""",
									        """2""",
									        """0""",
									        """0""",
									        """0""",
									        """,""",
									        SPIECE_UNDERLINE + """and""",
									        SPIECE_UNDERLINE + """this""",
									        SPIECE_UNDERLINE + """is""",
									        SPIECE_UNDERLINE + """f""",
									        """al""",
									        """s""",
									        """é""",
									        """.""",
									    ]  ,      )
									a_						:						List[str] 							= tokenizer.convert_tokens_to_ids(_lowercase    )
									self.assertListEqual(
									    _lowercase  ,      [
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									    ]  ,      )

									a_						:						List[Any] 							= tokenizer.convert_ids_to_tokens(_lowercase    )
									self.assertListEqual(
									    _lowercase  ,      [
									        SPIECE_UNDERLINE + """I""",
									        SPIECE_UNDERLINE + """was""",
									        SPIECE_UNDERLINE + """b""",
									        """or""",
									        """n""",
									        SPIECE_UNDERLINE + """in""",
									        SPIECE_UNDERLINE + """""",
									        """<unk>""",
									        """2""",
									        """0""",
									        """0""",
									        """0""",
									        """,""",
									        SPIECE_UNDERLINE + """and""",
									        SPIECE_UNDERLINE + """this""",
									        SPIECE_UNDERLINE + """is""",
									        SPIECE_UNDERLINE + """f""",
									        """al""",
									        """s""",
									        """<unk>""",
									        """.""",
									    ]  ,      )





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						Any 							= (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
															a_						:						Optional[int] 							= self.rust_tokenizer_class.from_pretrained(_lowercase  ,      **_lowercase    )
															a_						:						str 							= self.tokenizer_class.from_pretrained(_lowercase  ,      **_lowercase    )

															a_						:						Union[str, Any] 							= tempfile.mkdtemp()

															a_						:						Any 							= tokenizer_r.save_pretrained(_lowercase    )
															a_						:						Union[str, Any] 							= tokenizer_p.save_pretrained(_lowercase    )

															# Checks it save with the same files + the tokenizer.json file for the fast one
															self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files    )    )
															a_						:						Union[str, Any] 							= tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f    )
															self.assertSequenceEqual(_lowercase  ,      _lowercase    )

															# Checks everything loads correctly in the same way
															a_						:						Optional[int] 							= tokenizer_r.from_pretrained(_lowercase    )
															a_						:						Tuple 							= tokenizer_p.from_pretrained(_lowercase    )

															# Check special tokens are set accordingly on Rust and Python
															for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(_lowercase  ,      _lowercase    )    )

															shutil.rmtree(_lowercase    )

															# Save tokenizer rust, legacy_format=True
															a_						:						Any 							= tempfile.mkdtemp()

															a_						:						Dict 							= tokenizer_r.save_pretrained(_lowercase  ,      legacy_format=_lowercase    )
															a_						:						Optional[int] 							= tokenizer_p.save_pretrained(_lowercase    )

															# Checks it save with the same files
															self.assertSequenceEqual(_lowercase  ,      _lowercase    )

															# Checks everything loads correctly in the same way
															a_						:						str 							= tokenizer_r.from_pretrained(_lowercase    )
															a_						:						List[Any] 							= tokenizer_p.from_pretrained(_lowercase    )

															# Check special tokens are set accordingly on Rust and Python
															for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(_lowercase  ,      _lowercase    )    )

															shutil.rmtree(_lowercase    )

															# Save tokenizer rust, legacy_format=False
															a_						:						Optional[Any] 							= tempfile.mkdtemp()

															a_						:						List[Any] 							= tokenizer_r.save_pretrained(_lowercase  ,      legacy_format=_lowercase    )
															a_						:						int 							= tokenizer_p.save_pretrained(_lowercase    )

															# Checks it saved the tokenizer.json file
															self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files    )    )

															# Checks everything loads correctly in the same way
															a_						:						Optional[Any] 							= tokenizer_r.from_pretrained(_lowercase    )
															a_						:						Optional[Any] 							= tokenizer_p.from_pretrained(_lowercase    )

															# Check special tokens are set accordingly on Rust and Python
															for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(_lowercase  ,      _lowercase    )    )

															shutil.rmtree(_lowercase    )





						@require_torch
						def 			UpperCamelCase__					(    self    )     -> Any:
									if not self.test_seqaseq:
												return

									a_						:						List[str] 							= self.get_tokenizers()
									for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''    ):
															# Longer text that will definitely require truncation.
															a_						:						Optional[int] 							= [
															    """ UN Chief Says There Is No Military Solution in Syria""",
															    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
															    """ Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
															    """ will only worsen the violence and misery for millions of people.""",
															]
															a_						:						str 							= [
															    """Şeful ONU declară că nu există o soluţie militară în Siria""",
															    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
															    """ Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
															    """ că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
															]
															try:
																		a_						:						Union[str, Any] 							= tokenizer.prepare_seqaseq_batch(
																		    src_texts=_lowercase  ,      tgt_texts=_lowercase  ,      max_length=3  ,      max_target_length=10  ,      return_tensors="""pt"""  ,      src_lang="""eng_Latn"""  ,      tgt_lang="""ron_Latn"""  ,      )
															except NotImplementedError:
																		return
															self.assertEqual(batch.input_ids.shape[1]  ,      3    )
															self.assertEqual(batch.labels.shape[1]  ,      10    )
															# max_target_length will default to max_length if not specified
															a_						:						str 							= tokenizer.prepare_seqaseq_batch(
															    _lowercase  ,      tgt_texts=_lowercase  ,      max_length=3  ,      return_tensors="""pt"""    )
															self.assertEqual(batch.input_ids.shape[1]  ,      3    )
															self.assertEqual(batch.labels.shape[1]  ,      3    )

															a_						:						Optional[Any] 							= tokenizer.prepare_seqaseq_batch(
															    src_texts=_lowercase  ,      max_length=3  ,      max_target_length=10  ,      return_tensors="""pt"""    )
															self.assertEqual(batch_encoder_only.input_ids.shape[1]  ,      3    )
															self.assertEqual(batch_encoder_only.attention_mask.shape[1]  ,      3    )
															self.assertNotIn("""decoder_input_ids"""  ,      _lowercase    )





						@unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece."""    )
						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									pass





						def 			UpperCamelCase__					(    self    )     -> Dict:
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
															a_						:						Optional[Any] 							= [AddedToken("""<special>"""  ,      lstrip=_lowercase    )]

															a_						:						Optional[Any] 							= self.rust_tokenizer_class.from_pretrained(
															    _lowercase  ,      additional_special_tokens=_lowercase  ,      **_lowercase    )
															a_						:						int 							= tokenizer_r.encode("""Hey this is a <special> token"""    )

															a_						:						List[str] 							= tokenizer_r.encode("""<special>"""  ,      add_special_tokens=_lowercase    )[0]

															self.assertTrue(special_token_id in r_output    )

															if self.test_slow_tokenizer:
																		a_						:						Optional[Any] 							= self.rust_tokenizer_class.from_pretrained(
																		    _lowercase  ,      additional_special_tokens=_lowercase  ,      **_lowercase  ,      )
																		a_						:						List[Any] 							= self.tokenizer_class.from_pretrained(
																		    _lowercase  ,      additional_special_tokens=_lowercase  ,      **_lowercase    )

																		a_						:						Optional[Any] 							= tokenizer_p.encode("""Hey this is a <special> token"""    )

																		a_						:						List[str] 							= tokenizer_cr.encode("""Hey this is a <special> token"""    )

																		self.assertEqual(_lowercase  ,      _lowercase    )
																		self.assertEqual(_lowercase  ,      _lowercase    )
																		self.assertTrue(special_token_id in p_output    )
																		self.assertTrue(special_token_id in cr_output    )







@require_torch
@require_sentencepiece
@require_tokenizers
class        A__(unittest.TestCase							):
						"""simple docstring"""






						_A    :				Union[str, Any]       					=			'''facebook/nllb-200-distilled-600M'''
						_A    :				Dict       					=			[
						    ''' UN Chief Says There Is No Military Solution in Syria''',
						    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
						]
						_A    :				List[str]       					=			[
						    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
						    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
						    ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
						    ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
						]
						_A    :				Any       					=			[
						    256_047,
						    16_297,
						    134_408,
						    8_165,
						    248_066,
						    14_734,
						    950,
						    1_135,
						    105_721,
						    3_573,
						    83,
						    27_352,
						    108,
						    49_486,
						    2,
						]





						@classmethod
						def 			UpperCamelCase__					(    cls    )     -> Optional[Any]:
									a_						:						NllbTokenizer 							= NllbTokenizer.from_pretrained(
									    cls.checkpoint_name  ,      src_lang="""eng_Latn"""  ,      tgt_lang="""ron_Latn"""    )
									a_						:						Optional[int] 							= 1
									return cls





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""]  ,      256_001    )
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""]  ,      256_002    )
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""]  ,      256_057    )





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						Optional[Any] 							= self.tokenizer.batch_encode_plus(self.src_text    ).input_ids[0]
									self.assertListEqual(self.expected_src_tokens  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									self.assertIn(_lowercase  ,      self.tokenizer.all_special_ids    )
									# fmt: off
									a_						:						Optional[Any] 							= [RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
									# fmt: on

									a_						:						List[Any] 							= self.tokenizer.decode(_lowercase  ,      skip_special_tokens=_lowercase    )
									a_						:						Tuple 							= self.tokenizer.decode(generated_ids[1:]  ,      skip_special_tokens=_lowercase    )
									self.assertEqual(_lowercase  ,      _lowercase    )
									self.assertNotIn(self.tokenizer.eos_token  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_						:						Dict 							= ["""this is gunna be a long sentence """ * 20]
									assert isinstance(src_text[0]  ,      _lowercase    )
									a_						:						int 							= 10
									a_						:						List[str] 							= self.tokenizer(_lowercase  ,      max_length=_lowercase  ,      truncation=_lowercase    ).input_ids[0]
									self.assertEqual(ids[-1]  ,      2    )
									self.assertEqual(ids[0]  ,      _lowercase    )
									self.assertEqual(len(_lowercase    )  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]    )  ,      [256_203, 3]    )





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						Optional[Any] 							= tempfile.mkdtemp()
									a_						:						str 							= self.tokenizer.fairseq_tokens_to_ids
									self.tokenizer.save_pretrained(_lowercase    )
									a_						:						Union[str, Any] 							= NllbTokenizer.from_pretrained(_lowercase    )
									self.assertDictEqual(new_tok.fairseq_tokens_to_ids  ,      _lowercase    )





						@require_torch
						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						Optional[Any] 							= self.tokenizer(
									    self.src_text  ,      text_target=self.tgt_text  ,      padding=_lowercase  ,      truncation=_lowercase  ,      max_length=len(self.expected_src_tokens    )  ,      return_tensors="""pt"""  ,      )
									a_						:						List[str] 							= shift_tokens_right(
									    batch["""labels"""]  ,      self.tokenizer.pad_token_id  ,      self.tokenizer.lang_code_to_id["""ron_Latn"""]    )

									self.assertIsInstance(_lowercase  ,      _lowercase    )

									self.assertEqual((2, 15)  ,      batch.input_ids.shape    )
									self.assertEqual((2, 15)  ,      batch.attention_mask.shape    )
									a_						:						Optional[int] 							= batch.input_ids.tolist()[0]
									self.assertListEqual(self.expected_src_tokens  ,      _lowercase    )
									self.assertEqual(_lowercase  ,      batch.decoder_input_ids[0, 0]    )  # EOS
									# Test that special tokens are reset
									self.assertEqual(self.tokenizer.prefix_tokens  ,      [EN_CODE]    )
									self.assertEqual(self.tokenizer.suffix_tokens  ,      [self.tokenizer.eos_token_id]    )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Optional[Any] 							= self.tokenizer(self.src_text  ,      padding=_lowercase  ,      truncation=_lowercase  ,      max_length=3  ,      return_tensors="""pt"""    )
									a_						:						Optional[Any] 							= self.tokenizer(
									    text_target=self.tgt_text  ,      padding=_lowercase  ,      truncation=_lowercase  ,      max_length=10  ,      return_tensors="""pt"""    )
									a_						:						Union[str, Any] 							= targets["""input_ids"""]
									a_						:						Dict 							= shift_tokens_right(
									    _lowercase  ,      self.tokenizer.pad_token_id  ,      decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]  ,      )

									self.assertEqual(batch.input_ids.shape[1]  ,      3    )
									self.assertEqual(batch.decoder_input_ids.shape[1]  ,      10    )





						@require_torch
						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Any 							= self.tokenizer._build_translation_inputs(
									    """A test"""  ,      return_tensors="""pt"""  ,      src_lang="""eng_Latn"""  ,      tgt_lang="""fra_Latn"""    )

									self.assertEqual(
									    nested_simplify(_lowercase    )  ,      {
									        # A, test, EOS, en_XX
									        """input_ids""": [[256_047, 70, 7_356, 2]],
									        """attention_mask""": [[1, 1, 1, 1]],
									        # ar_AR
									        """forced_bos_token_id""": 256_057,
									    }  ,      )





						@require_torch
						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						str 							= True
									a_						:						Union[str, Any] 							= self.tokenizer(
									    """UN Chief says there is no military solution in Syria"""  ,      src_lang="""eng_Latn"""  ,      tgt_lang="""fra_Latn"""    )
									self.assertEqual(
									    inputs.input_ids  ,      [16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]    )

									a_						:						str 							= False
									a_						:						Union[str, Any] 							= self.tokenizer(
									    """UN Chief says there is no military solution in Syria"""  ,      src_lang="""eng_Latn"""  ,      tgt_lang="""fra_Latn"""    )
									self.assertEqual(
									    inputs.input_ids  ,      [256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]    )





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				:      int   		=			logging.get_logger(__name__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						str 							= DPTConfig(embedding_type="""hybrid""")

			if "large" in checkpoint_url:
						a_						:						List[str] 							= 1_0_2_4
						a_						:						List[str] 							= 4_0_9_6
						a_						:						Tuple 							= 2_4
						a_						:						str 							= 1_6
						a_						:						List[str] 							= [5, 1_1, 1_7, 2_3]
						a_						:						str 							= [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
						a_						:						Any 							= (1, 3_8_4, 3_8_4)

			if "nyu" or "midas" in checkpoint_url:
						a_						:						int 							= 7_6_8
						a_						:						List[str] 							= [1, 1, 1, 0.5]
						a_						:						Tuple 							= [2_5_6, 5_1_2, 7_6_8, 7_6_8]
						a_						:						Tuple 							= 1_5_0
						a_						:						Optional[Any] 							= 1_6
						a_						:						Union[str, Any] 							= (1, 3_8_4, 3_8_4)
						a_						:						Optional[int] 							= False
						a_						:						int 							= """project"""

			if "ade" in checkpoint_url:
						a_						:						Union[str, Any] 							= True
						a_						:						int 							= 7_6_8
						a_						:						Dict 							= [1, 1, 1, 0.5]
						a_						:						Optional[int] 							= 1_5_0
						a_						:						Tuple 							= 1_6
						a_						:						str 							= """huggingface/label-files"""
						a_						:						List[Any] 							= """ade20k-id2label.json"""
						a_						:						Tuple 							= json.load(open(cached_download(hf_hub_url(a__  ,	a__  ,	repo_type="""dataset"""))  ,	"""r"""))
						a_						:						Optional[Any] 							= {int(a__): v for k, v in idalabel.items()}
						a_						:						str 							= idalabel
						a_						:						Optional[Any] 							= {v: k for k, v in idalabel.items()}
						a_						:						Optional[int] 							= [1, 1_5_0, 4_8_0, 4_8_0]

			return config, expected_shape


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Any 							= ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if (
			    "pretrained.model" in name
			    and "cls_token" not in name
			    and "pos_embed" not in name
			    and "patch_embed" not in name
			):
						a_						:						Dict 							= name.replace("""pretrained.model"""  ,	"""dpt.encoder""")
			if "pretrained.model" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.model"""  ,	"""dpt.embeddings""")
			if "patch_embed" in name:
						a_						:						List[str] 							= name.replace("""patch_embed"""  ,	"""""")
			if "pos_embed" in name:
						a_						:						Tuple 							= name.replace("""pos_embed"""  ,	"""position_embeddings""")
			if "attn.proj" in name:
						a_						:						Optional[int] 							= name.replace("""attn.proj"""  ,	"""attention.output.dense""")
			if "proj" in name and "project" not in name:
						a_						:						Tuple 							= name.replace("""proj"""  ,	"""projection""")
			if "blocks" in name:
						a_						:						Any 							= name.replace("""blocks"""  ,	"""layer""")
			if "mlp.fc1" in name:
						a_						:						Tuple 							= name.replace("""mlp.fc1"""  ,	"""intermediate.dense""")
			if "mlp.fc2" in name:
						a_						:						List[str] 							= name.replace("""mlp.fc2"""  ,	"""output.dense""")
			if "norm1" in name and "backbone" not in name:
						a_						:						Dict 							= name.replace("""norm1"""  ,	"""layernorm_before""")
			if "norm2" in name and "backbone" not in name:
						a_						:						str 							= name.replace("""norm2"""  ,	"""layernorm_after""")
			if "scratch.output_conv" in name:
						a_						:						List[Any] 							= name.replace("""scratch.output_conv"""  ,	"""head""")
			if "scratch" in name:
						a_						:						Optional[int] 							= name.replace("""scratch"""  ,	"""neck""")
			if "layer1_rn" in name:
						a_						:						List[Any] 							= name.replace("""layer1_rn"""  ,	"""convs.0""")
			if "layer2_rn" in name:
						a_						:						Any 							= name.replace("""layer2_rn"""  ,	"""convs.1""")
			if "layer3_rn" in name:
						a_						:						List[Any] 							= name.replace("""layer3_rn"""  ,	"""convs.2""")
			if "layer4_rn" in name:
						a_						:						Optional[Any] 							= name.replace("""layer4_rn"""  ,	"""convs.3""")
			if "refinenet" in name:
						a_						:						Tuple 							= int(name[len("""neck.refinenet""") : len("""neck.refinenet""") + 1])
						# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
						a_						:						List[Any] 							= name.replace(f'''refinenet{layer_idx}'''  ,	f'''fusion_stage.layers.{abs(layer_idx-4)}''')
			if "out_conv" in name:
						a_						:						List[Any] 							= name.replace("""out_conv"""  ,	"""projection""")
			if "resConfUnit1" in name:
						a_						:						List[str] 							= name.replace("""resConfUnit1"""  ,	"""residual_layer1""")
			if "resConfUnit2" in name:
						a_						:						Optional[int] 							= name.replace("""resConfUnit2"""  ,	"""residual_layer2""")
			if "conv1" in name:
						a_						:						List[str] 							= name.replace("""conv1"""  ,	"""convolution1""")
			if "conv2" in name:
						a_						:						Optional[Any] 							= name.replace("""conv2"""  ,	"""convolution2""")
			# readout blocks
			if "pretrained.act_postprocess1.0.project.0" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.act_postprocess1.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.0.0""")
			if "pretrained.act_postprocess2.0.project.0" in name:
						a_						:						str 							= name.replace("""pretrained.act_postprocess2.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.1.0""")
			if "pretrained.act_postprocess3.0.project.0" in name:
						a_						:						List[str] 							= name.replace("""pretrained.act_postprocess3.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.2.0""")
			if "pretrained.act_postprocess4.0.project.0" in name:
						a_						:						Any 							= name.replace("""pretrained.act_postprocess4.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.3.0""")

			# resize blocks
			if "pretrained.act_postprocess1.3" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess1.3"""  ,	"""neck.reassemble_stage.layers.0.projection""")
			if "pretrained.act_postprocess1.4" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess1.4"""  ,	"""neck.reassemble_stage.layers.0.resize""")
			if "pretrained.act_postprocess2.3" in name:
						a_						:						int 							= name.replace("""pretrained.act_postprocess2.3"""  ,	"""neck.reassemble_stage.layers.1.projection""")
			if "pretrained.act_postprocess2.4" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.act_postprocess2.4"""  ,	"""neck.reassemble_stage.layers.1.resize""")
			if "pretrained.act_postprocess3.3" in name:
						a_						:						Union[str, Any] 							= name.replace("""pretrained.act_postprocess3.3"""  ,	"""neck.reassemble_stage.layers.2.projection""")
			if "pretrained.act_postprocess4.3" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess4.3"""  ,	"""neck.reassemble_stage.layers.3.projection""")
			if "pretrained.act_postprocess4.4" in name:
						a_						:						Tuple 							= name.replace("""pretrained.act_postprocess4.4"""  ,	"""neck.reassemble_stage.layers.3.resize""")
			if "pretrained" in name:
						a_						:						List[Any] 							= name.replace("""pretrained"""  ,	"""dpt""")
			if "bn" in name:
						a_						:						Optional[int] 							= name.replace("""bn"""  ,	"""batch_norm""")
			if "head" in name:
						a_						:						Union[str, Any] 							= name.replace("""head"""  ,	"""head.head""")
			if "encoder.norm" in name:
						a_						:						Any 							= name.replace("""encoder.norm"""  ,	"""layernorm""")
			if "auxlayer" in name:
						a_						:						Any 							= name.replace("""auxlayer"""  ,	"""auxiliary_head.head""")
			if "backbone" in name:
						a_						:						Optional[Any] 							= name.replace("""backbone"""  ,	"""backbone.bit.encoder""")

			if ".." in name:
						a_						:						str 							= name.replace(""".."""  ,	""".""")

			if "stem.conv" in name:
						a_						:						Union[str, Any] 							= name.replace("""stem.conv"""  ,	"""bit.embedder.convolution""")
			if "blocks" in name:
						a_						:						Union[str, Any] 							= name.replace("""blocks"""  ,	"""layers""")
			if "convolution" in name and "backbone" in name:
						a_						:						Dict 							= name.replace("""convolution"""  ,	"""conv""")
			if "layer" in name and "backbone" in name:
						a_						:						Optional[int] 							= name.replace("""layer"""  ,	"""layers""")
			if "backbone.bit.encoder.bit" in name:
						a_						:						Union[str, Any] 							= name.replace("""backbone.bit.encoder.bit"""  ,	"""backbone.bit""")
			if "embedder.conv" in name:
						a_						:						List[str] 							= name.replace("""embedder.conv"""  ,	"""embedder.convolution""")
			if "backbone.bit.encoder.stem.norm" in name:
						a_						:						str 							= name.replace("""backbone.bit.encoder.stem.norm"""  ,	"""backbone.bit.embedder.norm""")
			return name


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			for i in range(config.num_hidden_layers):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						a_						:						Optional[int] 							= state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''')
						a_						:						str 							= state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''')
						# next, add query, keys and values (in that order) to the state dict
						a_						:						Any 							= in_proj_weight[: config.hidden_size, :]
						a_						:						List[str] 							= in_proj_bias[: config.hidden_size]
						a_						:						List[str] 							= in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						a_						:						str 							= in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						a_						:						Any 							= in_proj_weight[
						    -config.hidden_size :, :
						]
						a_						:						List[str] 							= in_proj_bias[-config.hidden_size :]


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Dict 							= """http://images.cocodataset.org/val2017/000000039769.jpg"""
			a_						:						Union[str, Any] 							= Image.open(requests.get(a__  ,	stream=a__).raw)
			return im


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_							,					a_						:						str 							= get_dpt_config(a__)
			# load original state_dict from URL
			# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
			a_						:						str 							= torch.load(a__  ,	map_location="""cpu""")
			# remove certain keys
			remove_ignore_keys_(a__)
			# rename keys
			for key in state_dict.copy().keys():
						a_						:						List[str] 							= state_dict.pop(a__)
						a_						:						str 							= val
			# read in qkv matrices
			read_in_q_k_v(a__  ,	a__)

			# load HuggingFace model
			a_						:						Any 							= DPTForSemanticSegmentation(a__) if """ade""" in checkpoint_url else DPTForDepthEstimation(a__)
			model.load_state_dict(a__)
			model.eval()

			# Check outputs on an image
			a_						:						Any 							= 4_8_0 if """ade""" in checkpoint_url else 3_8_4
			a_						:						Any 							= DPTImageProcessor(size=a__)

			a_						:						Dict 							= prepare_img()
			a_						:						int 							= image_processor(a__  ,	return_tensors="""pt""")

			# forward pass
			a_						:						Optional[Any] 							= model(**a__).logits if """ade""" in checkpoint_url else model(**a__).predicted_depth

			if show_prediction:
						a_						:						List[str] 							= (
						    torch.nn.functional.interpolate(
						        outputs.unsqueeze(1)  ,	size=(image.size[1], image.size[0])  ,	mode="""bicubic"""  ,	align_corners=a__  ,	)
						    .squeeze()
						    .cpu()
						    .numpy()
						)

						Image.fromarray((prediction / prediction.max()) * 2_5_5).show()

			if pytorch_dump_folder_path is not None:
						Path(a__).mkdir(exist_ok=a__)
						print(f'''Saving model to {pytorch_dump_folder_path}''')
						model.save_pretrained(a__)
						print(f'''Saving image processor to {pytorch_dump_folder_path}''')
						image_processor.save_pretrained(a__)

			if push_to_hub:
						model.push_to_hub("""ybelkada/dpt-hybrid-midas""")
						image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""")


if __name__ == "__main__":
	__snake_case				:      str   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
	    type=str,
	    help="""URL of the original DPT checkpoint you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default=None,
	    type=str,
	    required=False,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	)
	parser.add_argument(
	    """--model_name""",
	    default="""dpt-large""",
	    type=str,
	    help="""Name of the model, in case you're pushing to the hub.""",
	)
	parser.add_argument(
	    """--show_prediction""",
	    action="""store_true""",
	)

	__snake_case				:      str   		=			parser.parse_args()
	convert_dpt_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
	)


